1. Field of the Invention
The invention lies in the field of interactive television programming. Specifically, the invention pertains to a method and system for TV user profile data prediction and modeling, to a method and system for program and/or advertisement program preference determination, to a method and system for targeted advertising for television and interactive television based on the profile data prediction, modeling and preference determination, and to a method and system with which a complete program sequence can be presented to the viewer based on the preference determination and stored programming. The latter will be referred to as automatic program sequence (Virtual Channel) creation and the virtual channel will be presented as a separate channel in the electronic programming guide (EPG).
2. Description of the Prior Art
Systems and methods to target advertising in interactive television are known. The prior art systems and methods generally target advertising through a statistically sampled, program driven mechanism. Advertising for television is priced in accordance with the rating of a certain program and time slot. Advertisements must be placed so that they reach the intended target audience. The more audience a certain program delivers, and the more clearly focused that audience is with regard to the demographic information, the higher the price for placing the advertisement. By far the most popular TV ratings system currently in use in the United States is Nielsen Media Research. The Nielsen ratings and share system is based on a 5000 member national sample and approximately 50 local market samples. The information gleaned from the national sample is based on a measurement of which program is watched at a certain time in a given television household and by which members of the household. The latter information is determined via so-called People Meters that are installed in the sample households and via which the viewers indicate when they are watching TV at a certain time by pushing a button individually assigned to them. The national sample utilizes rather crude demographic information to define preference ratings for the program determination. The results are published via ratings that are defined relative to the statistical universe (e.g., all television households, male 20 to 40 years, etc.) and by shares. The latter represent a percentage of the universe members watching a given program at the time of its broadcast. A slightly more accurate system, referred to as the Portable People Meter, is currently being tested in a limited local television market by Arbitron. The Portable People Meter is a pager-sized electronic transceiver that records a person's television usage via inaudible codes that are superimposed on television programs. At the end of the day, the transceiver is placed on a base station, from which the recorded information is then sent to a central data processing facility.
In the context of TV user profile data prediction and modeling, the prior art methods and systems do not use program arrival and departure frequency and click timing as preference indicators. Preference ratings in the context of programming predictions are thus rather rudimentary. Since prior art systems do not model transitions, sequential program behavior, and temporal program utilization in a general predictive architecture, they are unable to predict a user's preference based on sophisticated content and temporal relationships.
By not assessing when there is adequate evidence to infer a preference, known methods tend to incorrectly predict user preferences, or they may wait too long before building higher confidence. Known classification methods require that all feature dimensions of a sample be correlated to the observation, and then assume a Gaussian distribution parameterization to describe group clusters. However, this is inaccurate as the data are not generally subject to normal distribution.
In the context of program or advertising program preference determination, the prior art methods do not have an automatic user input, and thus no method of learning which metrics best predict a certain user's preference. Further, if preference ratings are available for a given demographic group, they are only stationarily weighted and no dynamic weighting adjustment is effected.
In the context of targeted advertising for television and interactive television, the prior art methods principally use demographic information, not contextual behavioral information as part of the user targeting profile. This reduces targeting performance in non-demographically classifiable consumer groups, and demographic inferring accuracy.